Methods and apparatus for performing reduced complexity discrete fourier transforms using interpolation

ABSTRACT

Methods and apparatus arc provided for performing reduced complexity discrete Fourier transforms using interpolation An input sequence of length N is transformed by extending the input sequence to an extended input sequence of length M, where M is greater than N (a power of two greater than N); performing a discrete Fourier Transform (DFT), such as a power-of-two DFT, on the extended input sequence to obtain an interpolated sequence; and applying a conversion matrix to the interpolated sequence to obtain a DFT output for the input sequence of length N. The input sequence of length N can be extended to an extended input sequence of length M, for example, by employing a zero padding technique, a cyclic extension technique, a windowing of a cyclic extended sequence technique or a resampling-based interpolation technique to extend the input sequence The conversion matrix is substantially a spar se matrix.

FIELD OF THE INVENTION

The present invention relates generally to Fourier transform techniques,and more particularly, to techniques for performing a discrete Fouriertransform with reduced computational complexity

BACKGROUND OF THE INVENTION

The Foxier transform maps functions to other functions. Generally, theFouier transform decomposes a function into a continuous spectrum of itsfrequency components. Likewise, the inverse Fourier transformsynthesizes a function from its spectrum of frequency components. Thediscrete Fourier transform (DFT) is a transform for Fourier analysis offinite-domain discrete-time signals.

The DFT of a vector r of length N is defined as follows:

${y_{k} = {{\frac{1}{\sqrt{N}}{\sum\limits_{n = 0}^{N - 1}{^{{- \frac{2\pi \; }{N}}{kn}}r_{n}\mspace{14mu} {where}\mspace{14mu} k}}} = 0}},\ldots \mspace{11mu},{N - 1.}$

See, for example, E. C. Ifeachot and B. W. Jervis, “Digital SignalProcessing: A Practical Approach,” Addison-Wesley, June 1993; orhttp://en.wikipedia.org/wiki/Discerete_fourier_transform Evaluatingthese sums directly would take on the order of N² arithmeticaloperations. A fast Fourier transform (FFT) algorithm reduces thecomplexity to an order of N log N operations In general, such algorithmsdepend upon the factorization of N, so for most values of N, the FFTcomplexity is on the order of N log N.

One popular FFT implementation is the Cooley-Tukey algorithm Generally,the Cooley-Tukey algorithm recursively breaks down a DFT of anycomposite size N equal to N1 N2 into many smaller DFTs of sizes N1 andN2, along with order of N multiplications by complex toots of unity Themost well-known use of the Cooley-Tukey algorithm divides the transforminto two pieces of size N/2 at each step, and is therefore limited topower-of-two sizes In general, however, any factorization can be used,such as radix-2 and mixed-radix algorithms

While such existing FFT algorithms efficiently decompose a function oflength N into a discrete spectrum of its frequency components for manyvalues of N, brute force DFTs must still generally be used when N is alarge prime number. A need therefore exists for improved methods andapparatus that approximate the DFT result for a large prime number

SUMMARY OF THE INVENTION

Generally, methods and apparatus are provided for performing reducedcomplexity discrete Fourier transforms using interpolation. According toa first aspect of the invention, an input sequence of length N istransformed by extending the input sequence to an extended inputsequence of length M, where M is greater than N (a power of two greaterthan N); performing a discrete Fourier Transform (DFT), such as apower-of-two DFT, on the extended input sequence to obtain aninterpolated sequence; and applying a conversion matrix to theinterpolated sequence to obtain a DFT output for the input sequence oflength N.

The input sequence of length N can be extended to an extended inputsequence of length M, for example, by employing a zero paddingtechnique, a cyclic extension technique, a windowing of a cyclicextended sequence technique or a resampling-based interpolationtechnique to extend the input sequence According to another aspect ofthe invention, the conversion matrix is substantially a sparse matrix.

A more complete understanding of the present invention, as well asfurther features and advantages of the present invention, will beobtained by reference to the following detailed description anddrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates the evaluation of a DFT on a finite-domaindiscrete-time input signal, to decompose the input function into acontinuous spectrum of its frequency components;

FIG. 2 is a flow chart describing an exemplary implementation of a DFTmethod 200 incorporating features of the present invention;

FIG. 3 illustrates the square of the magnitude of the elements in anexemplary row of a conversion matrix Q;

FIG. 4 illustrates the square of the magnitude of all the elements inthe conversion matrix Q; and

FIGS. 5 through 8 are tables illustrating the complexity analysis forthe computation of exemplary 839 (N) point DFTs using the four exemplarysequence extension approaches

DETAILED DESCRIPTION

The present invention provides methods and apparatus for performingreduced complexity discrete Fourier transforms using interpolation FIG.1 illustrates the evaluation of a DPT 100 on a finite-domaindiscrete-time input signal, to decompose the input function into adiscrete spectrum of its frequency components. As previously indicated,the DFT 100 of a vector r of length N can be defined as follows:

$\begin{matrix}{{y_{k} = {{\frac{1}{\sqrt{N}}{\sum\limits_{n = 0}^{N - 1}{^{{- \frac{2\pi \; }{N}}{kn}}r_{n}\mspace{14mu} {where}\mspace{14mu} k}}} = 0}},\ldots \mspace{11mu},{N - 1.}} & (1)\end{matrix}$

Equation 1 can also be written as follows:

y _(N×1) =F _(N×N) r _(N×1)

where all the elements of matrix F are defined by

$^{{- \frac{2{\pi }}{N}}{kn}}$

for k=0, . . . , N−1 and n=0, . . . , N−1.

Evaluating DFT based on equation (2) would take O(N²) arithmeticoperations However, by using a radix-2 algorithm, the DFT can beevaluated by O(N×log₂N) operations.

Simplification of DFT by Interpolation

The present invention recognizes that a DFT can be obtained with reducedcomplexity using interpolation. FIG. 2 is a flow chart describing anexemplary implementation of a DFT method 200 incorporating features ofthe present invention. As shown in FIG. 2, the DFT method 200 initiallyobtains the finite-domain discrete-time input signal of length N, duringstep 210 The DFT method 200 then extends the finite-domain discrete-timeinput signal to a length M during step 220 In four exemplary embodimentsdiscussed herein, the extension may be performed using a zero padding,cyclic extension, windowing of a cyclic extended sequence and resamplebased interpolation technique For example, when N is not a power of two,the input signal can be extended to a length M, which is a power of twogreater than N.

During step 230, the DFT method 200 performs a DFT, such as apower-of-two DFT, on the extended input signal to obtain interpolatedsequence Finally during step 240, a conversion matrix is applied to theinterpolated sequence to obtain the ideal DFT output.

The DFT method 200 may also be expressed using the followingpseudo-code:

Step 1: Extend non-power-of-two sequence (r) of length N to apower-of-two sequence of length M by performing zero padding or cyclicextension (r′)

Step 2: Perform Radix-2 FFI operation on r′.

Step 3: Compute a conversion matrix (Q) and apply Q to obtain the DFT ofr (i.e , x=Qr′=Fr where F is the DFT matrix).

Step 4: Obtain an approximation of x (x′) using a simplified version ofQ (Q_(t)). Q_(t) has only T nonzero elements per row.

x′=Q_(t)r′

Zero Padding

As indicated above, the DFT method 200 extends the finite-domaindiscrete-time input signal of length N to a length M. In an exemplaryzero padding embodiment, for values of N that ate not equal to a powerof two, zeros are added to the end of the input sequence so that totallength of the sequence is equal to a power of two. This leads to aninterpolated DFT result in accordance with the present invention.Equation 2 can be written as follows, where M is a number equal to apower of two:

y _(M×1) =W _(M×M) r _(M×1)   (3)

The same result can also be obtained using the following equation:

y _(M×1) =Z _(M×M) U _(zM×N) r _(N×1)   (4)

where all of the elements of matrix Z (an FFI of the input sequence) aredefined by

$^{{- \frac{2{\pi }}{M}}{km}}$

and k=0, . . . M−1, m=0, . . . , M−1 and

$U_{{zM} \times N} = \begin{bmatrix}I_{N \times N} \\0_{M - {N \times N}}\end{bmatrix}$

where I and 0 are the identity and null matrices. Thus, equations 3 and4 provide the FFT of the extended input sequence.

The DFT of the original sequence (having a length N that is not a powerof two), can be obtained from y (the FFT of the extended input sequence)utilizing a conversion matrix Q Conversion matrix, Q, is used to convertany power of two DFT sequence to a non power of two DFT sequence.

$\begin{matrix}{\quad\begin{matrix}{y_{N \times 1} = {Q_{N \times M}y_{M \times 1}}} \\{= {Q_{N \times M}Z_{M \times M}U_{{zM} \times N}r_{N \times 1}}} \\{= {Q_{N \times M}W_{{zM} \times N}r_{N \times 1}}}\end{matrix}} & (5)\end{matrix}$

From Equations (2) and (5),

F=QW_(z)   (6)

Alternatively, equation (6) can be expressed as follows:

Q=FW_(z) ^(t)

where W_(z) ^(t) is the pseudo inverse of W_(z) and is defined by (W_(z)^(H)W_(z))⁻¹W_(z) ^(H) and W_(z) ^(H) is the complex conjugate of thetransposed matrix of W_(z). W_(z) ^(H)W_(z) is an identity matrix.Therefore, W_(z) ^(t)=W_(z) ^(H) and Q=FW_(z) ^(H) The elements in Q canbe computed using the following equation:

$\begin{matrix}{q_{m,n} = {\frac{1}{\sqrt{MN}}{\sum\limits_{l = 0}^{N - 1}^{2{j\pi}\; {l{({\frac{m}{M} - \frac{n}{N}})}}}}}} & (7)\end{matrix}$

where m=0, . . . , M−1 and n=0. . . , N−1

The term

$\sum\limits_{l = 0}^{N - 1}^{2{j\pi}\; {l{({\frac{m}{M}\frac{n}{N}})}}}$

represents a geometric progression (GP) of N elements.The sum of p elements of a GP is defined as follows:

$\begin{matrix}{{\sum\limits_{p = 0}^{P - 1}\; {af}^{p}} = {a\frac{1 - f^{P}}{1 - f}}} & (8)\end{matrix}$

Hence, the elements of matrix Q can be defined as shown in the followingequation:

$\begin{matrix}{q_{({m,n})} = {\frac{1}{\sqrt{MN}}\frac{1 - ^{j\; {AN}}}{1 - ^{j\; A}}}} & (9)\end{matrix}$

where

${A = {2{\pi \left( {\frac{m}{M} - \frac{n}{N}} \right)}}},$

m 32 0, . . . , M−1 and n=0, . . . , N−1 except fox q(1,1) which isequal to N. The magnitude of q is defined as

${q} = {\sqrt{\frac{1 - {\cos ({NA})}}{1 - {\cos (A)}}}.}$

FIG. 3 is a plot 300 illustrating the square of the magnitude of theelements in the exemplary 420^(th) row of the conversion matrix Q It isnoted that the pattern observed in the magnitude square of Q elementsfor all the rows is similar to the plot 300 shown in FIG. 3 FIG. 4 is aplot 400 illustrating the square of the magnitude of all the elements inthe conversion matrix Q, for exemplary values of N equal to 839 and Mequal to 1024. As shown in FIG. 4, the conversion matfix Q isessentially a sparse matrix having the magnitude primarily concentratedon the diagonal, with the other elements having near-zero values Thus,there are relatively few elements in the matrix, resulting in areasonable number of computations

Cyclic Extension of the Sequence

As indicated above, the DFT method 200 extends the finite-domaindiscrete-time input signal of length N to a length M In an exemplarycyclic extension embodiment, for values of N that are not equal to apower of two, a cyclic extension can be performed on the sequence tomake its length equal to power of two. This operation can be performed,for example, by multiplying the input sequence with a matrix U_(c), asfollows:

$\begin{matrix}{U_{{cM} \times N} = \begin{bmatrix}I_{N \times N} \\{I_{M - {N \times M} - N}\mspace{20mu} 0_{M - {N \times 2N} - M}}\end{bmatrix}} & (10)\end{matrix}$

The interpolated sequence obtained by performing a power-of-two DFT onthe cyclic extended sequence is given by the following equation:

y _(c)′_(M×1) =Z _(cM×M) U _(cM×N) r _(N×1) =W _(cM×N) r _(N×1)   (11)

Z_(c) can be represented as Z_(cM×M)=[z_(c1) z_(c2) . . . z_(cM)] wherez_(c1), z_(c2), and z_(cM) are vectors each of length M representingeach column in Z. Then, W_(c) can be expressed as follows:

$\begin{matrix}{\quad\begin{matrix}{W_{{cM} \times N} = {Z_{{cM} \times M}U_{{cM} \times N}}} \\{= \left\lbrack {z_{c\; 1} + {z_{{cN} + 1}\mspace{14mu} z_{c\; 2}} + {z_{{cN} + 2}\mspace{11mu} \cdots \mspace{11mu} z_{{cM} - N}} + {z_{cM}\mspace{14mu} z_{{cM} - N + 1}\mspace{11mu} {\cdots z}_{cM}}} \right\rbrack}\end{matrix}} & (12)\end{matrix}$

The DFT of the original input sequence can be obtained by multiplyingy_(c)′ by the conversion matrix Q as follows:

$\begin{matrix}{\quad\begin{matrix}{y_{N \times 1} = {Q_{c\mspace{11mu} N \times M}y_{c\mspace{11mu} M \times 1}}} \\{= {Q_{c\mspace{11mu} N \times M}W_{c\mspace{11mu} M \times N}r_{N \times 1}}}\end{matrix}} & (13)\end{matrix}$

From equations (2) and (13),

F=Q_(c)W_(c)

Alternatively,

Q_(t)=FW_(c) ^(t).

where W_(c) ^(t) is the pseudo inverse of W_(c) and is defined by (W_(c)^(H)W_(c))⁻¹W_(c) ^(H) and W_(c) ^(H) is the complex conjugate of thetransposed matrix of W_(c). W_(c) ^(H)W_(c) is a diagonal matrix wherethe first M−N elements along the diagonal are equal to ½ and the valueof the remaining elements is 1. In addition:

${\left( {W_{c}^{H}W_{c}} \right)^{- 1}W_{c}^{H}} = \left\lbrack {\frac{z_{c\; 1} + z_{{c\; N} + 1}}{2}\frac{z_{c\; 2} + z_{{c\; N} + 2}}{2}\mspace{11mu} \ldots \mspace{11mu} \frac{z_{{cM} - N} + z_{cM}}{2}\mspace{11mu} z_{{cM} - N + 1}z_{cM}} \right\rbrack^{H}$

The conversion matrix Q_(c) for the cyclic extension embodiment may beexpressed as follows:

$\begin{matrix}{\quad\begin{matrix}{Q_{c} = {{F\left( {W_{c}^{H}W_{c}} \right)}^{- 1}W_{c}^{H}}} \\{= {F\left\lbrack {\frac{z_{c\; 1} + z_{{cN} + 1}}{2}\frac{z_{c\; 2} + z_{{cN} + 2}}{2}\mspace{11mu} \ldots \mspace{11mu} \frac{z_{{cM} - N} + z_{cM}}{2}z_{{cM} - N + 1}z_{cN}} \right\rbrack}^{H}}\end{matrix}} & (14)\end{matrix}$

The elements of the conversion matrix Q is given by the followingequation:

$\begin{matrix}{\left. \Rightarrow q_{{cm},n} \right. = \left\{ {{\frac{1}{\sqrt{MN}}{\sum\limits_{l = 0}^{M - N - 1}\; \frac{^{2{j\pi}\; {l{({\frac{m}{M} - \frac{n}{N}})}}} + ^{2{{j\pi}({({\frac{{({l + N})}m}{M}\frac{nl}{N}})}}}}{2}}} + {\sum\limits_{l = {M - N}}^{N - 1}\; ^{2{{j\pi l}{({\frac{m}{M} - \frac{n}{N}})}}}}} \right.} & (15)\end{matrix}$

wherein m=0, . . . M−1 and n=0, . . . , N−1.

Equation (15) can be split into three parts, each of which is aGeometric Progression (GP) as follows:

$\begin{matrix}{q_{{cm},n} = {\frac{1}{\sqrt{MN}}{\quad\left\lbrack {{\frac{1}{2} \times \frac{1 - ^{j\; {A{({M - N})}}}}{1 - ^{j\; A}}} + {\frac{1}{2} \times \frac{1 - ^{{j{({A + \frac{Nm}{M}})}}{({M - N})}}}{1 - ^{j{({A + \frac{Nm}{M}})}}}} + \frac{^{j\; {A{({M - N})}}} - ^{j\; {AN}}}{1 - ^{j\; A}}} \right\rbrack}}} & (16)\end{matrix}$

Windowing of a Cyclic Extended Sequence

As indicated above, the DFT method 200 extends the finite-domaindiscrete-time input signal of length N to a length M In an exemplarywindowing of a cyclic extended sequence, for values of N that ate notequal to a power of two, a cyclic extension can be performed on thesequence to make its length equal to power of two and more A windowingoperation can be performed on the cyclic extended sequence to obtain thesequence of required length. Here, for example, for an 839 point DFT,the length of the cyclic extension is chosen to be 2048 This operationcan be performed by multiplying the sequence with a matrix U_(w),defined as follows:

$\begin{matrix}{U_{{wM} \times N} = \begin{bmatrix}I_{N \times N} \\I_{N \times N} \\{I_{M - {N \times M} - N}\mspace{11mu} 0_{M - {N \times 2N} - M}}\end{bmatrix}} & (17)\end{matrix}$

The interpolated sequence obtained by performing power-of-two DFT on thewindowed cyclic extended sequence is given as follows:

y _(w)′_(M×1) =Z _(wM×M) D _(M×M) U _(wM×N) r _(N×1)   (18)

where U_(w) is the matrix to define the cyclic extension of the sequencegiven by Equation (19) and D (shown below in Equation (20)) is thematrix used to apply a windowing operation on the cyclic extended inputsequence:

$\begin{matrix}{U_{{wM} \times N} = \begin{bmatrix}I_{N \times N} \\I_{N \times N} \\{I_{M - {N \times M} - N}\mspace{11mu} 0_{M - {2N \times 3N} - M}}\end{bmatrix}} & (19) \\{D_{M \times M} = \begin{bmatrix}d_{1} & \; & \; & \; & \; \\\; & d_{2} & \; & 0 & \; \\\; & \; & ⋰ & \; & \; \\\; & 0 & \; & d_{M - 1} & \; \\\; & \; & \; & \; & d_{M}\end{bmatrix}} & (20)\end{matrix}$

The DFT of the original sequence can be obtained by multiplying y_(w)′by the conversion matrix Q_(w) as follows:

$\begin{matrix}{\quad\begin{matrix}{y_{{wN} \times 1} = {Q_{w\; N \times M}y_{w\; M \times 1}^{\prime}}} \\{= {Q_{w\; N \times M}Z_{w\; M \times M}D_{w\; M \times M}U_{w\; M \times N}r_{N \times 1}}}\end{matrix}} & (21)\end{matrix}$

From equations (2) and (21),

F=Q_(w)Z_(w)D_(w)U_(w)

Alternatively:

Q _(w) =F(Z _(w) D _(w) U _(w))^(t)

where (Z_(w)D_(w)U_(w))^(t) is the pseudo inverse of (Z_(w)D_(w)U_(w))and is defined by((Z_(w)D_(w)U_(w))^(H)(Z_(w)D_(w)U_(w)))⁻¹(Z_(w)D_(w)U_(w))^(H).

$\begin{matrix}{\quad\begin{matrix}{Q_{w} = {F\left( {Z_{w}D_{w}U_{w}} \right)}^{\dagger}} \\{= {{F\left( {U_{w}^{H}D_{w}^{H}Z_{w}^{H}Z_{w}D_{w}U_{w}} \right)}^{- 1}U_{w}^{H}D_{w}^{H}Z_{w}^{H}}} \\{= {{F\left( {U_{w}^{H}D_{w}^{2}U_{w}} \right)}^{- 1}U_{w}^{H}D_{w}^{H}Z_{w}^{H}}}\end{matrix}} & (22)\end{matrix}$

where (U_(w) ^(H)D_(w) ²U_(w))⁻¹ is defined as follow:

$\begin{matrix}{\quad\begin{matrix}{\quad{\quad{\left( {U_{w}^{H}D_{w}^{2}U_{w}} \right)^{- 1} = \begin{bmatrix}\begin{bmatrix}I_{1} & 0_{1} & I_{1} & 0_{1} & I_{3} \\0_{2} & I_{2} & 0_{2} & I_{2} & 0_{3}\end{bmatrix} \\{\begin{bmatrix}D_{1}^{2} & 0 & 0 & 0 & 0 \\0 & D_{2}^{2} & 0 & 0 & 0 \\0 & 0 & D_{3}^{2} & 0 & 0 \\0 & 0 & 0 & D_{4}^{2} & 0 \\0 & 0 & 0 & 0 & D_{5}^{2}\end{bmatrix}\begin{bmatrix}I_{4} & 0_{5} \\0_{4} & I_{5} \\I_{4} & 0_{5} \\0_{4} & I_{5} \\I_{6} & 0_{6}\end{bmatrix}}\end{bmatrix}^{- 1}}}} \\{= \begin{bmatrix}\left( {D_{1}^{2} + D_{3}^{2} + D_{5}^{2}} \right)^{- 1} & 0 \\0 & \left( {D_{2}^{2} + D_{4}^{2}} \right)^{- 1}\end{bmatrix}}\end{matrix}} & (23)\end{matrix}$

where I₁, I₂, I₃, I₄, I₅, & I₆ are identity matrices of sizes M−2N×N/2,3N−M×N/2, M−2N×M−2N, N/2×M−2N, N/2×2N & 2N×2N, 0 ₁, 0 ₂, 0 ₃, 0 ₄, 0 ₅,& 0 ₆ are null matrices of sizes M−2N×N/2, 3N−M×N/2, 3N−M×M−2N,N/2×M−2N, N/2×2N & 2N×2N, D₁ ², D₂ ², D₃ ², D₄ ² awe the diagonalmatrices of D of sizes N/2×N/2 and D₅ ², M−2N×M−2N, respectively

The conversion matrix Q_(c) may be expressed as follows:

$\begin{matrix}{Q = {{{F\begin{bmatrix}\left( {D_{1}^{2} + D_{3}^{2} + D_{5}^{2}} \right)^{- 1} & 0 \\0 & \left( {D_{2}^{2} + D_{4}^{2}} \right)^{- 1}\end{bmatrix}}\begin{bmatrix}D_{1}^{2} & 0 & D_{3}^{2} & 0 & D_{5}^{2} \\0 & D_{2}^{2} & 0 & D_{4}^{2} & 0\end{bmatrix}}Z^{H}}} & (24)\end{matrix}$

Resample Based Interpolation

As indicated above, the DFT method 200 extends the finite-domaindiscrete-time input signal of length N to a length M. In an exemplaryresample based interpolation, for any non power of 2 sequence (r) oflength N, a cyclic extension can be performed on the sequence to makeits length equal to power of 2 (M) and mote. The transformed sequence(y′) is obtained by performing FFI on r. To obtain an approximation ofy. Resample is performed on y′ with special attention paid to eliminatethe edge effect. Resample is the combination of interpolation anddecimation to change the sampling rate by a rational factor The resamplefunction samples the input sequence at N/M times the original samplerate using a polyphase implementation. During a resampling process,anti-aliasing (low pass) FIR filter via Kaiser window is used tocompensate for the filter delay The filter length can be changed andwith increase in the filter length the accuracy increases at the expenseof additional computation complexity. This operation is performedoffline and only the filter taps (twice the filter length) need to bestored and can be used for on chip computations

The resampling operation can be expressed in matrix form as follows

y _(rN×1) =D _(N×MN) H _(MN×MN) U _(MN×M) y _(M×1)

where U_(MN×M) is the upsample-by-N matrix, H_(MN×MN) is theinterpolation lowpass filter and D_(N×MN) is the downsample-by-M matrix:

$\begin{bmatrix}y_{r_{0}} \\\vdots \\y_{r_{N - 1}}\end{bmatrix} = {{\begin{bmatrix}{1\mspace{20mu} 0_{1 \times {({{MN} - 1})}}} \\\vdots \\{0_{1 \times {({kM})}}\mspace{20mu} 1\mspace{20mu} 0_{1 \times {({{MN} - {kM} - 1})}}} \\\vdots \\{0_{1 \times {({{({N - 1})}M})}}\mspace{20mu} 1\mspace{20mu} 0_{1 \times {({M - 1})}}}\end{bmatrix}\begin{bmatrix}h_{0} & h_{- 1} & h_{- 2} & \cdots & h_{1} \\h_{1} & h_{0} & h_{- 1} & \cdots & h_{2} \\h_{2} & h_{1} & h_{0} & \cdots & h_{3} \\\vdots & \vdots & \vdots & \vdots & \vdots \\h_{{MN} - 1} & h_{{MN} - 2} & h_{{MN} - 3} & \cdots & h_{0}\end{bmatrix}}{\quad{\begin{bmatrix}{1\mspace{20mu} 0\mspace{20mu} \cdots \mspace{20mu} 0\mspace{20mu} 0} \\0_{{({N - 1})} \times M} \\{0\mspace{20mu} 1\mspace{20mu} \cdots \mspace{20mu} 0\mspace{20mu} 0} \\0_{{({N - 1})} \times M} \\\vdots \\{0\mspace{20mu} 0\mspace{20mu} \cdots \mspace{20mu} 0\mspace{20mu} 1} \\0_{{({N - 1})} \times M}\end{bmatrix}\begin{bmatrix}y_{0}^{\prime} \\\vdots \\y_{M - 1}\end{bmatrix}}}}$

where index k=0 . . . N−1 is the low index for matrix D_(N×MN). Theelements of H_(MN×MN) are defined as h_(n)=h_(n+NM) and h_(n)=0, forLN≦|n|≦MN/2 Therefore, it can be observed that most of the entries in PImatrix are zeros.

$\begin{bmatrix}y_{r_{0}} \\\vdots \\y_{r_{N - 1}}\end{bmatrix} = {\quad{\begin{bmatrix}h_{0} & h_{- N} & h_{2N} & \cdots & h_{{- {({M - 1})}}N} \\h_{M} & h_{M - N} & h_{M - {2N}} & \cdots & h_{M - {{({M - 1})}N}} \\h_{2M} & h_{{2M} - N} & h_{{2M} - {2N}} & \cdots & h_{{2M} - {{({M - 1})}N}} \\\vdots & \vdots & \vdots & \vdots & \vdots \\h_{{({N - 1})}M} & h_{{{({N - 1})}M} - N} & h_{{{({N - 1})}M} - {2N}} & \cdots & h_{{{({N - 1})}M} - {{({M - 1})}N}}\end{bmatrix}{\quad\begin{bmatrix}y_{0} \\\vdots \\y_{M - 1}\end{bmatrix}}}}$

Approximation of Conversion Matrix Q

In a further variation of the present invention, rather than consideringall of the elements in each row of conversion matrix Q, it is sufficientto use some elements (taps) around the point at which the magnitude of Qis maximum Therefore, the conversion matrix can be approximated to Q′ ofsize N×T These elements contribute maximum energy when compared to otherelements in that row. The magnitude of Q attains its maximum value whenA approaches zero. This happens at m_(max) equal to M×n/N. The sum ofall the elements in each

row of Q is one, i e.,

${{\sum\limits_{m = 0}^{M}{Q\left( {n,m} \right)}}} = 1$

By considering only T elements in each row of Q, some part of the energyis lost. The amount of energy captured by selecting a few elements ineach row can as be expressed as follows,

$\begin{matrix}{{E_{C}(n)} = \frac{\sum\limits_{m_{\max} - v}^{m_{\max} + v}\; {{Q\left( {n,m} \right)}}^{2}}{\sum\limits_{m = 0}^{M}{{Q\left( {n,m} \right)}}^{2}}} & (25)\end{matrix}$

where v=(T−1)/2, I is number of taps and m_(max) is the column locationof each row at which the square of the magnitude of Q is maximum For agiven M and N, only the required elements in Q (T elements in each row)can be computed offline and stored in a look up table, which can be usedfor on chip computations The number of MAC operations required for thecomputation of an N-point DFT for cyclic and zero padded input sequencesis M×log₂M+T×N and for windowing cyclic extended input sequences, thenumber of required MAC operations is M×log₂M+I×N+M For small values ofI, this number is considerably less than the N² operations required fora brute force implementation of N-point DFT

The complexity analysis for the computation of the exemplary 839 (N)point DFTs using the four exemplary sequence extension approaches areshown in FIGS. 5 through 8. It is noted that the savings incomputational complexity is with respect to a classical DFT computation.The output SNR is the ratio of the mean of the signal power divided bythe mean of the noise power. The error is the error energy of thedifference between the actual and approximated DFT signals. The variable‘bits’ in the tables of FIGS 5-8 represent the accuracy of theapproximated signal in comparison to the actual DFT signal and isdefined as SNR(dB)/6. The mean and variance of the input data are alsoshown in FIGS. 5-8.

From FIGS. 5-8, it can be observed that resample based interpolationgives the best performance among the four exemplary embodiments. Forexample, when the number of taps is 15, more than 20 bit accuracy isobtained This accuracy is obtained with more than 96% savings in thecomputational complexity compared to a direct 839-point DFT operation.

While exemplary embodiments of the present invention have been describedwith respect to digital logic blocks or software algorithms (or acombination thereof), as would be apparent to one skilled in the art,various functions may be implemented in the digital domain as processingsteps in a software program, in hardware by circuit elements or statemachines, or in combination of both software and hardwarc. Such softwaremay be employed in, for example, a digital signal processor,micro-controller, or general-purpose computer Such hardware and softwaremay be embodied within circuits implemented within an integrated circuitThus, the functions of the present invention can be embodied in the formof methods and apparatuses for practicing those methods One or moreaspects of the present invention can be embodied in the for of programcode, for example, whether stored in a storage medium, loaded intoand/or executed by a machine, or transmitted over some transmissionmedium, wherein, when the program code is loaded into and executed by amachine, such as a computer; the machine becomes an apparatus forpracticing the invention. When implemented on a general-purposeprocessor, the program code segments combine with the processor toprovide a device that operates analogously to specific logic circuitsThe invention can also be implemented in one or more of an integratedcircuit, a digital signal processor, a microprocessor, and amicro-controller.

As is known in the art, the methods and apparatus discussed herein maybe distributed as an article of manufacture that itself comprises acomputer readable medium having computer readable code means embodiedthereon. The computer readable program code means is operable, inconjunction with a computer system, to carry out all or some of thesteps to perform the methods or create the apparatuses discussed herein.The computer readable medium may be a recordable medium (e.g., floppydisks, hard drives, compact disks, memory cards, semiconductor devices,chips, application specific integrated circuits (ASICs)) or may be atransmission medium (e.g., a network comprising fiber-optics, theworld-wide web, cables, or a wireless channel using time-divisionmultiple access, code-division multiple access, or other radio-frequencychannel). Any medium known or developed that can store informationsuitable for use with a computer system may be used. Thecomputer-readable code means is any mechanism for allowing a computer toread instructions and data, such as magnetic variations on a magneticmedia or height variations on the surface of a compact disk.

The computer systems and servers described herein each contain a memorythat will configure associated processors to implement the methods,steps, and functions disclosed herein. The memories could be distributedor local and the processors could be distributed or singular Thememories could be implemented as an electrical, magnetic or opticalmemory, or any combination of these or other types of storage devices.Moreover, the term “memory” should be construed broadly enough toencompass any information able to be read from or written to an addressin the addressable space accessed by an associated processor. With thisdefinition, information on a network is still within a memory becausethe associated processor can retrieve the information from the network.

It is to be understood that the embodiments and variations shown anddescribed herein are merely illustrative of the principles of thisinvention and that various modifications may be implemented by thoseskilled in the art without departing from the scope and spirit of theinvention.

1. A method for transforming an input sequence of length N, comprising:extending said input sequence to an extended input sequence of length M,where M is greater than N; performing a discrete Fourier Transform (DFT)on said extended input sequence to obtain an interpolated sequence; andapplying a conversion matrix to said interpolated sequence to obtain aDFT output for said input sequence of length N.
 2. The method of claim1, wherein M is a power of two greater than N.
 3. The method of claim 1,wherein said extending step further comprises the step of employing azero padding technique to extend said input sequence.
 4. The method ofclaim 1, wherein said extending step further comprises the step ofemploying a cyclic extension technique to extend said input sequence 5.The method of claim 1, wherein said extending step further comprises thestep of employing a windowing of a cyclic extended sequence technique toextend said input sequence
 6. The method of claim 1, wherein saidextending step further comprises the step of employing a resample-basedinterpolation technique to extend said input sequence.
 7. The method ofclaim 1, wherein said conversion matrix is substantially a sparsematrix.
 8. A system for transforming an input sequence of length N,comprising: a memory; and at least one processor, coupled to the memory,operative to: extend said input sequence to an extended input sequenceof length M, where M is greater than N; perform a discrete FourierTransform (DFT) on said extended input sequence to obtain aninterpolated sequence; and apply a conversion matrix to saidinterpolated sequence to obtain a DPT output for said input sequence oflength N.
 9. The system of claim 8, wherein M is a power of two greaterthan N.
 10. The system of claim 8, wherein said input sequence isextended by employing a zero padding technique to extend said inputsequence
 11. The system of claim 8, wherein said input sequence isextended by employing a cyclic extension technique to extend said inputsequence
 12. The system of claim 8, wherein said input sequence isextended by employing a windowing of a cyclic extended sequencetechnique to extend said input sequence.
 13. The system of claim 8,wherein said conversion matrix is substantially a sparse matrix.
 14. Acircuit for transforming an input sequence of length N, comprising: afirst logic block for extending said input sequence to an extended inputsequence of length M, where M is greater than N; a second logic blockfor performing a discrete Fourier Transform (DFT) on said extended inputsequence to obtain an interpolated sequence; and a third logic block forapplying a conversion matrix to said interpolated sequence to obtain aDFT output for said input sequence of length N
 15. The circuit of claim14, wherein M is a power of two greater than N.
 16. The circuit of claim14, wherein said input sequence is extended by employing a zero paddingtechnique to extend said input sequence
 17. The circuit of claim 14,wherein said input sequence is extended by employing a cyclic extensiontechnique to extend said input sequence.
 18. The circuit of claim 14,wherein said input sequence is extended by employing a windowing of acyclic extended sequence technique to extend said input sequence
 19. Thecircuit of claim 14, wherein said conversion matrix is substantially asparse matrix
 20. An article of manufacture for transforming an inputsequence of length N, comprising a machine readable medium containingone or mote programs which when executed implement the steps of:extending said input sequence to an extended input sequence of length M,where M is greater than N; performing a discrete Fourier Transform (DFT)on said extended input sequence to obtain an interpolated sequence; andapplying a conversion matrix to said interpolated sequence to obtain aDFT output for said input sequence of length N